Similarity of emerging market returns under changing market conditions: Markets in the ASEAN-4, Latin America,Middle East,and BRICs

We studied the risk-return distances of 18 emerging stock markets in the period from January 2000 to December 2013. Distances are linked to volatility and time-varying correlations estimated in standard and asymmetric DCC models. Our results revealed a positive relationship between risk-return distances and volatility, which means that during more volatile periods, the risk-return characteristics in emerging markets exhibit lower similarity to the characteristics found in developed markets. This result seems to be in sharp contrast to most empirical studies using correlations. Within the portfolio framework, our results suggest that diversification into emerging stock markets may still provide desirable benefits to international investors.     

Deregulation and liberalization of the Chinese stock market and the improvement of market efficiency

This study employs single and multiple variance ratio tests to reexamine the weak-form efficient market hypothesis (EMH) of A- and B-shares on the Shanghai and Shenzhen exchanges in Chinese stock market. The study also examines the influence of the release of investment restriction of B-share markets on market efficiency. For the whole sample period, the weak-form EMH is only supported for Shanghai A-shares, and is not supported for the remaining shares. For the sub-sample period, the Shenzhen A-share and B-shares of both exchanges being rejected for the weak-form EMH in the earlier sample period are supported following the regulatory change. Rolling multiple variance ratio test statistic values provide additional evidence of weak-form EMH. The improvement of market efficiency can be explained by the increased liquidity and maturity accompanying deregulation and liberalization.     

The uncertainty in extreme risk forecasts from covariate-augmented volatility models

For a GARCH-type volatility model with covariates, we derive asymptotically valid forecast intervals for risk measures, such as the Value-at-Risk or Expected Shortfall. To forecast these, we use estimators from extreme value theory. In the volatility model, we allow for leverage effects and the inclusion of exogenous variables, e.g., volatility indices or high-frequency volatility measures. In simulations, we find coverage of the forecast intervals to be adequate for sufficiently extreme risk levels and sufficiently large samples, which is consistent with theory. Finally, we investigate if covariate information from volatility indices or high-frequency data improves risk forecasts for major US stock indices. While—in our framework—volatility indices appear to be helpful in this regard, intra-day data are not.     

证券民事赔偿新规及其对证券监管的影响

文章针对最高人民法院于2003年1月9日颁布的《关于审理证券市场因虚假陈诉引发的民事赔偿案件的若干规定》中关于案件受理前置条件的规定,进行了探讨,并认为虽然还存在有争议的地方,但却全面的影响了证券市场的监管。     

Realized volatility forecasting: Robustness to measurement errors

In this paper, we suggest how to handle the issue of the heteroskedasticity of measurement errors when specifying dynamic models for the conditional expectation of realized variance. We show that either adding a GARCH correction within an asymmetric extension of the $\mathsf{HAR}$  class ($\mathsf{AHAR}$-$\mathsf{GARCH}$), or working within the class of asymmetric multiplicative error models ($\mathsf{AMEM}$) greatly reduces the need for quarticity/quadratic terms to capture attenuation bias. This feature in $\mathsf{AMEM}$ can be strengthened by considering regime specific dynamics. Model Confidence Sets confirm this robustness both in- and out-of-sample for a panel of 28 big caps and the S&P500 index.     

Information flows within and across sectors in Chinese stock markets

We examine the patterns of information flows within and across sectors of the two Chinese stock exchanges in Shanghai and Shenzhen during 1994–2001. Using the generalized forecast error variance decomposition, we find a high degree of interdependence, indicating that the sectors are highly integrated and sector prices reflect information from other sectors. Industry is the most influential sector in both exchanges, while Finance in Shenzhen is the least integrated with other sectors. Implications of the findings for investors and policymakers are also discussed.     

Shifts in volatility driven by large stock market shocks

This paper presents an extension of the stochastic volatility model which allows for level shifts in volatility of stock market returns, known as structural breaks. These shifts are endogenously driven by large return shocks (innovations), reflecting large pieces of market news. These shocks are identified from the data as being bigger in absolute terms than the values of two threshold parameters of the model: one for the negative shocks and one for the positive shocks. The model can be employed to investigate different sources of stock market volatility shifts driven by market news, without relying on exogenous information. In addition to this, it has a number of interesting features which enable us to study the effects of large return shocks on future levels of market volatility. The above properties of the model are shown based on a study for the US stock market volatility.     

GARCH-Type modeling of stock market returns for the pre-crisis countries

Empirical studies have shown that a large number of financial asset returns exhibit fat tails （leptokurtosis） and are often characterized by volatility clustering and asymmetry. This paper considers the ability of the GARCH-Type （Generalized Autoregressive Conditional Heteroskedasticity） models to capture the stylized features of volatility in national stock market returns for three countries （Portugal, Spain and Greece）. The results of this paper suggest that in the presence of asymmetric responses to innovations in the market, the ARMA （1,1）-GJRGARCH（1,1） skewed Student-t model which accommodates both the skewness and the kurtosis of financial time series is preferred.     

论另类股票市场的功能

融资渠道是中小高科技创新型企业发展所面临的主要问题,而创业投资又面对出口问题。文章认为另类股票市场能较有效地解决这两个问题。     

Independent component analysis for realized volatility: Analysis of the stock market crash of 2008

This paper investigates the factors that drove the U.S. equity market returns from 2007 to early 2010. The period was highlighted by volatile energy and commodity prices, the collapse of insurance and banking firms, extreme implied volatility and a subsequent rally in the overall market. To extract the driving factors, we decompose the returns of the S&P500 sector ETFs into statistically independent signals using independent component analysis. We find that the generated factors have interesting financial interpretations and are consistent with the major economic themes of the period. We find that there are two sets of general market betas during the period along with a dominant factor for energy and materials sector. In addition, we find that the EGARCH model which accommodates asymmetric responses between returns and volatility can plausibly fit the high levels of variance during the crash. Finally, estimated correlations dropped when commodity prices moved higher, but then spiked when the S&P500 crashed in late 2008.     

The costs of suboptimal dynamic asset allocation: General results and applications to interest rate risk, stock volatility risk, and growth/value tilts

The recent theoretical asset allocation literature has derived optimal dynamic investment strategies in various advanced models of asset returns. But how sensitive is investor welfare to deviations from the theoretically optimal strategy? Will unsophisticated investors do almost as well as sophisticated investors? This paper develops a general theoretical framework for answering such questions and applies it to three specific models of interest rate risk, stochastic stock volatility, and mean reversion and growth/value tilts of stock portfolios. Among other things, we find that growth/value tilts are highly valuable, but the hedging of time-varying stock risk premia is less important.